References of "Schwickart, Tim Klemens 50003064"
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See detailA Fast Model-Predictive Speed Controller for Minimised Charge Consumption of Electric Vehicles
Schwickart, Tim Klemens UL; Voos, Holger UL; Hadji-Minaglou, Jean-Régis UL et al

in Asian Journal of Control (2016), 18(5),

This paper presents the design of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles including the driving speed reference generation. The controller is ... [more ▼]

This paper presents the design of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles including the driving speed reference generation. The controller is designed to meet the properties of a series-production electric vehicle whose characteristics are identified and validated by measurements. The predictive eco-cruise controller aims at finding the best compromise between speed-reference tracking and energy consumption of the vehicle using an underlying dynamic model of the vehicle motion and charge consumption. The originally non-linear motion model is transformed into a linear model mainly by using a coordinate transform. To obtain a piecewise linear approximation of the charge consumption map, the measured characteristics are approximated by a convex piecewise linear function represented as the maximum of a set of linear constraint functions. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the closed-loop operation finally illustrate the effectiveness of the approach. [less ▲]

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See detailA Flexible Move Blocking Strategy to Speed up Model-Predictive Control while Retaining a High Tracking Performance
Schwickart, Tim Klemens UL; Voos, Holger UL; Darouach, Mohamed et al

in 2016 European Control Conference (ECC), Aalborg, Denmark (2016, June)

This paper presents a strategy to reduce the complexity and thus the computational burden in modelpredictive control (MPC) by a flexible online input move blocking algorithm. Model-predictive sampled-data ... [more ▼]

This paper presents a strategy to reduce the complexity and thus the computational burden in modelpredictive control (MPC) by a flexible online input move blocking algorithm. Model-predictive sampled-data control of constrained, LTI plants is considered. Move blocking is an input parameterisation in MPC where the control input is forced to be constant over several prediction sample steps to reduce the dimensionality of the underlying optimisation problem. Typically, the prediction sample steps where the control input is not allowed to vary (i. e. the block distribution) is predetermined offline and is kept constant throughout the control operation. However, the control performance and computational efficiency can be improved if the block length is adjusted to the specific operating conditions. In this work, a heuristic method to adjust the block length online according to the initial state of the system, reference signals, measured disturbances and constraints is presented. A numerical example shows the effectiveness of the approach. [less ▲]

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See detailEnergy-Efficient Driver Assistance System for Electric Vehicles Using Model-Predictive Control
Schwickart, Tim Klemens UL

Doctoral thesis (2015)

This thesis investigates a method to save energy and thus also extend the range of a series-production battery electric vehicle by influencing the driving style automatically with the help of a of a ... [more ▼]

This thesis investigates a method to save energy and thus also extend the range of a series-production battery electric vehicle by influencing the driving style automatically with the help of a of a cruise controller. An exploration of existing methods shows that the contextual consideration of the current and upcoming driving situation is necessary to realise safe and energy-efficient driving. This limits the appropriate approaches to online methods using updated predictions of the vehicle behaviour. It turns out that the most suitable method for the intended purpose is model-predictive control (MPC). The MPC generates controls for the accelerator pedal of the vehicle based on optimised predictions of the vehicle motion and energy consumption subject to the current and future road slope, curvature, speed limits and distance to an eventually preceding vehicle. The non-linear nature of the vehicle dynamics generally necessitates the use of a non-linear prediction model and solving a non-linear optimisation which goes along with difficulties in the online real-time implementation. However in this work - by exploiting and extending the tool sets of classical MPC - a controller based on a quadratic optimal control problem with linear constraints can be formulated that approximates the nonlinearities of the plant dynamics with equivalent accuracy as a non-linear formulation. A linear prediction model of the vehicle motion is derived by a change of the model domain from time to position and a change of variables to predict the kinetic energy of the moving vehicle instead of the driving speed. Further, a convex piece-wise linear energy consumption model is included in the inequality constraints of the problem according to the methodology of separable programming to capture the consumption characteristics of the vehicle in different operating points. In this form, real-time capability and the energy-saving potential of the presented control approach can be demonstrated by simulations of the closed loop and by implementing the controller for driving experiments. A Smart ED series-production battery electric vehicle is chosen for the practical tests and all models and parameters are identified and adapted to the characteristics of the car. In this application case, a significant energy-saving potential could be demonstrated compared to human drivers. To further reduce the computational burden and speed up the computation, the so-called move blocking method for input parameterisation of the MPC control trajectory is investigated and extended within this work to a flexible move blocking approach which enables a fast computation and at the same time high tracking performance. [less ▲]

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See detailDesign and Simulation of a Real-Time Implementable Energy-Efficient Model-Predictive Cruise Controller for Electric Vehicles
Schwickart, Tim Klemens UL; Voos, Holger UL; Minaglou, Jean-Régis UL et al

in Journal of the Franklin Institute (2015), 352(2), 603-625

This paper presents the design of a novel energy-efficient model-predictive cruise controller for electric vehicles as well a simulation model of the longitudinal vehicle dynamics and its energy ... [more ▼]

This paper presents the design of a novel energy-efficient model-predictive cruise controller for electric vehicles as well a simulation model of the longitudinal vehicle dynamics and its energy consumption. Both, the controller and the dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified and verified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. Considering the nonlinearities is essential for a proper prediction of the system states over the prediction horizon to achieve the desired energy-saving behaviour. In this work, the vehicle motion equation is reformulated in terms of the kinetic energy of the moving vehicle which leads to a linear differential equation without loss of information. The energy consumption is modeled implicitly by exploiting the special form of the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept. [less ▲]

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See detailFahrerassistenzsystem zur vorausschauenden energieeffizienten Geschwindigkeitsregelung speziell für Elektrofahrzeuge
Schwickart, Tim Klemens UL; Voos, Holger UL

in Tagungsband VDI Tagung Fahrerassistenzsysteme (2014, October 14)

Eines der größten Probleme heutiger Elektrofahrzeuge ist nach wie vor die limitierte Reichweite, die fast ausschließlich auf die technisch begrenzte Akkukapazität zurückgeführt werden kann. Baldige ... [more ▼]

Eines der größten Probleme heutiger Elektrofahrzeuge ist nach wie vor die limitierte Reichweite, die fast ausschließlich auf die technisch begrenzte Akkukapazität zurückgeführt werden kann. Baldige substanzielle Fortschritte im Bereich der Akku-Technologie sind fragwürdig und eine Effizienzsteigerung des Fahrzeuges selbst ist durch bereits jetzt sehr hohe Wirkungsgrade (ca. 85 %) kaum mehr möglich. Ein lohnendes Feld zur Reichweitenverlängerung ist jedoch der Fahrstil selbst - hier verschwenden ungeübte oder unbewusste Fahrer einen großen Teil an Energie durch eine nicht vorausschauende Fahrweise, die beispielsweise zu vermeidbaren Bremsmanövern führt. In neuesten Elektrofahrzeugen wie dem e-Golf wurde dies bereits in ers-ten Ansätzen berücksichtigt. So kann der Fahrer dort zwischen verschiedenen Modi auswählen (z.B. Eco-Modus, Eco-Plus-Modus), hierbei wird jedoch lediglich die Fahrleistung an sich ge-drosselt und damit ein geringerer Energieverbrauch erreicht. In diesem Beitrag wird ein speziell auf Elektrofahrzeuge zugeschnittenes Fahrerassistenzsystem zur optimierungsbasierten vorausschauenden und energieeffizienten Geschwindigkeitsregelung vorgestellt. Basierend auf einem dynamischen Modell des Energieverbrauchs des Fahrzeugs, Informationen zur zukünftigen Fahrstrecke aus Karten (elektronischer Horizont) und In-formationen zu anderen Verkehrsteilnehmern (etwa durch Radar-Abstandsmessung zum vo-rausfahrenden Fahrzeug) kann das Fahrzeugverhalten durch Lösung eines Optimalsteuerungs-problems so geplant werden, dass sich für einen geeignet gewählten Prädiktionshorizont der beste Kompromiss aus Energieverbrauch und Fahrgeschwindigkeit ergibt. Nach dem Prinzip der Modellprädiktiven Regelung wird dieses Optimalsteuerungsproblem in jedem Zeitschritt ak-tualisiert und erneut gelöst. Die Besonderheit des vorgestellten Systems ist hierbei die spezielle Formulierung des Optimal-steuerungsproblems. Durch eine Koordinatentransformation wird die Fahrzeugbewegung mittels der kinetischen Energie des Fahrzeugs anstelle der Geschwindigkeit beschrieben. Weiterhin wird durch eine konvexe stückweise lineare Approximation des Verbrauchskennfelds eine besonders günstige mathematische Problemformulierung in Form eines quadratischen Programms mit linearen Beschränkungen erreicht. Dies ermöglicht im Gegensatz zu den meisten nichtlinearen Optimalsteuerungsansätzen eine Lösung in Echtzeit und mit garantierter Konvergenz. Zur experimentellen Validierung wird dieses Fahrerassistenzsystem aktuell für einen Smart Electric Drive realisiert. Dazu wurde das dynamische Energieverbrauchsmodell des Smart-ED in umfangreichen Prüfstandversuchen identifiziert und das Fahrzeug zur Realisierung der Assistenzfunktion technisch umgerüstet. [less ▲]

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See detailA Real-Time Implementable Model-Predictive Cruise Controller for Electric Vehicles and Energy-Efficient Driving
Schwickart, Tim Klemens UL; Voos, Holger UL; Darouach, Mohamed

in IEEE Multi-Conference on Systems and Control (MSC), Antibes, France, Oct. 2014 (2014, October)

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. The controller and the underlying dynamic model are designed to meet the properties of a ... [more ▼]

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption map is approximated by the maximum of a set of linear functions which is implicitly determined in the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller in closed-loop operation finally show the effectiveness of the approach. [less ▲]

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See detailA Novel Model-Predictive Cruise Controller for Electric Vehicles and Energy-Efficient Driving
Schwickart, Tim Klemens UL; Voos, Holger UL; Minaglou, Jean-Régis UL et al

in A Novel Model-Predictive Cruise Controller for Electric Vehicles and Energy-Efficient Driving (2014, July)

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. A predictive eco-cruise controller involves the minimisation of a compromise between terms ... [more ▼]

This paper presents a novel energy-efficient model-predictive cruise control formulation for electric vehicles. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. In this work, a coordinate transformation is used which leads to a linear differential motion equation without loss of information. The energy consumption is modeled by the maximum of a set of linear functions which is determined implicitly by the optimisation problem and thus leads to a piecewise linear model. The reformulations finally result in a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. The controller and the underlying dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified by measurements. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept. [less ▲]

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See detailAn Efficient Nonlinear Model-Predictive Eco-Cruise Control for Electric Vehicles
Schwickart, Tim Klemens UL; Voos, Holger UL; Hadji-Minaglou, Jean-Régis UL et al

in 11th IEEE International Conference on Industrial Informatics, Bochum, Germany, 29-31 July 2013 (2013, July)

A nonlinear problem formulation of an energy-saving model-predictive eco-cruise controller for electric vehicles is presented. With regard to the intended application in real-world tests, the model has to ... [more ▼]

A nonlinear problem formulation of an energy-saving model-predictive eco-cruise controller for electric vehicles is presented. With regard to the intended application in real-world tests, the model has to include the specific properties of a serial electric vehicle such as energy-recovery and a discontinuous accelerator input giving rise to a binary control variable. These specific features and the nonlinearity of the system dynamics make it a challenging task to formulate the optimisation problem in a way that allows a fast computation in real-time application. The challenges are addressed by using a model of the vehicle dynamics that is formulated in terms of the vehicle position instead of time and by considering the kinetic energy instead of the velocity. Furthermore, various constraints on the input and state variables are introduced for a realistic representation of the vehicle characteristics. A special focus is put on the treatment of a binary input variable in the optimisation. Here, in order to avoid a mixed-integer formulation of the problem, a continuous variable is introduced which is forced to take only discrete values by a penalty term. Finally, first simulation results underline the feasibility of this control approach. [less ▲]

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